Position: Synthetic Persona Needs Explicit Grounding and Standardized Reporting

This position paper argues that synthetic personas used in Large Language Models (LLM) social simulations need explicit grounding. By grounding, we mean that researchers should state where persona information comes from, what social actor or population it is meant to represent, what evidence supports it, and what kinds of simulation claims it can justify. Without such grounding, persona-based simulations risk treating appearance of plausible descriptions or vivid narratives as evidence of realism. We identify several recurring failure modes that explicit grounding can help expose, and argue that these failures should not be diagnosed only after simulation results are produced; they should be addressed at the level of persona construction and reporting. We conclude by proposing lightweight reporting standards for persona provenance, construction process, selection or sampling logic, internal consistency checks, model enactment checks, and intended inference.